Search Results (1,883)

This data descriptor provides a standardized and reproducible subsystem-level representation of the NREL wind turbine gearbox condition monitoring benchmarking dataset. The released records are derived from Healthy (H1–H10) and Damaged (D1–D10) measurement files and include subsystem-level standardized indices (KHI_HS, KHI_IMS, KHI_PL) together with a calibrated 0–1 Gearbox Health Index (GHI). The indices are generated using a fully specified and deterministic feature extraction and aggregation workflow based on established vibration indicators and healthy-referenced normalization. The Zenodo deposit contains machine-readable CSV tables intended to support transparent benchmarking across supervised classification and anomaly detection studies. The proposed GHI is introduced as an interpretable and reproducible reference baseline rather than an optimized diagnostic model. Technical validation demonstrates condition-level separability within the analyzed dataset while emphasizing the descriptive nature of the index. By releasing structured derived records and a documented regeneration procedure, this work enables an implementation-independent comparison of gearbox condition monitoring approaches and supports reproducible evaluation of alternative health index formulations. Full article

Fault diagnosis of rolling bearings in wind turbines is significantly challenged by strong noise, non-stationary signals, and multi-source interference. To address these issues, a Multi-Scale Attention Residual Graph Convolutional Network (MSAR-GCN) is proposed. First, a fully connected graph is constructed in the frequency domain using a temporal segmentation strategy, which preserves full spectral resolution and captures cross-frequency coupling features via node embeddings. Second, a multi-scale residual module with a cross-layer pyramid structure is designed to extract features at varying granularities, integrated with a dynamic multi-head attention mechanism to adaptively emphasize damage-sensitive frequency bands. Additionally, a hierarchical feature distillation mechanism is employed to compress high-dimensional features, ensuring model lightweighting while retaining critical fault information. Experimental validations on CWRU and JNU datasets demonstrate that MSAR-GCN achieves 97.02% and 92.5% accuracy under −10 dB Gaussian noise, respectively, outperforming existing methods by over 4%. Specifically, the model exhibits exceptional robustness, maintaining 93.09% accuracy under severe non-Gaussian impulsive noise. With verified feature separability and high computational efficiency, the proposed method offers a promising solution for high-precision, real-time industrial fault diagnosis. Full article

This study investigates the wind field characteristics of long-span suspension bridges in mountain valleys terrain, with a particular focus on the disturbance effects caused by bridge structure on wind measurements. Field data are collected using the Wind3D 6000 LiDAR installed near the bridge. By comparing wind field characteristics before and after bridge completion, this study evaluates the influence of the bridge structure on both mean and turbulent wind characteristics. The findings show that the presence of the bridge tower and deck reduces the measured mean wind speed and modifies its probability distribution. The bridge tower increases the effective ground roughness coefficient, thereby attenuating the vertical wind speed gradient. In addition, the bridge tower raises the measured turbulence intensity, alters its probability distribution, and decreases the agreement between the turbulent wind power spectrum and the von Kármán spectrum. It is necessary to correct the data affected by these disturbances to improve the accuracy of wind load assessments for long-span bridges, thus enhancing the reliability of bridge structural operation. Full article

The northeast monsoon prevailing over southeastern China in late seasons, generally from October to March, frequently generates water level anomalies upstream of the Taiwan Strait (TWS) that reach the coastal waters of Guangdong in South China, and, with compounding astronomical high tides, elevate coastal flood risk over the region. The risk of coastal flooding or sea inundation is further heightened when monsoon forcing co-occurs with storm surge brought by late-season tropical cyclones (TCs). This study integrates tide gauge observations from Hong Kong (HK) and its vicinity together with Delft3D Flexible Mesh simulations to diagnose a tide-modulated anomaly wave mechanism. Observations show that anomalies originating in or near TWS arrive in HK with station-dependent phasing. These water level anomalies exhibit a characteristic ~6 h periodicity west of the Taiwan Shoal, and display peaks that systematically align with the astronomical high tide. Time–frequency analysis reveals a wave period transformation from ~12 h north of Dongshandao over the coast of southeastern China to ~6 h west of the Taiwan Shoal. We test the hypothesis that wind-forced water anomalies generated in or near TWS undergo shoal-modulated nonlinear tide–wind interaction and tidal-current advection that transform their dominant period and phase-lock them to the tide, producing four anomaly peaks per day downstream and station-dependent phasing in HK. Hindcasts of the November 2024 monsoon episode reproduce the observed timing, periodicity, and spatial transition, while constituent experiments demonstrate that semi-diurnal forcing entering via the TWS is the primary driver of the ~6 h signal, with the Taiwan Shoal acting as the modulation locus. Accurate water level forecasts for the Guangdong coast, therefore, need to incorporate upstream wind forcing over the TWS and bathymetric controls around the Taiwan Shoal, with practical implications for compound flood risk during spring tides and co-occurring monsoon and/or TC events. Full article

The transition to smart cities is accelerating distributed wind and solar deployment. However, their intermittency challenges grid operation, thereby making accurate machine-learning-based prediction of wind speed and global horizontal irradiance (GHI) crucial. This study presents a cost-effective approach that enhances prediction accuracy by extracting additional features from timestamp records for deep learning models used to forecast GHI and wind speed. Unlike conventional methods that require onsite meteorological measurements, the proposed approach uses only date and time information as inputs to multivariate deep neural networks, including recurrent neural networks, gated recurrent units, long short-term memory (LSTM), bidirectional LSTM, and convolutional neural networks. For wind speed prediction, the proposed configuration achieves R² up to 0.9987, with RMSE as low as 0.067 m/s for 3 d ahead forecasting, outperforming univariate baselines and matching models. For GHI forecasting, the time-based configuration attains R² values above 0.9994 in 12 h ahead predictions, with the RMSE reduced to approximately 4.47 W/m², representing a substantial improvement over univariate models. The proposed framework maintains strong performance, particularly under clear and sunny conditions. These results demonstrate that timestamp-engineered features can deliver forecasting accuracy comparable to conventional multivariate meteorological models while significantly reducing infrastructure requirements, making the approach well-suited for scalable smart city energy management. Full article

Spray deposition and coverage within soybean canopies remain critical challenges for achieving effective pesticide applications, particularly under windy conditions. This research investigated the influence of wind speed, boom travel direction relative to wind direction, and nozzle type on droplet deposition, coverage uniformity, canopy penetration, and airflow distributions inside soybean canopies under controlled wind-tunnel airflow. Spray deposition, analyzed using a fluorometric tracer, and coverage, quantified with water-sensitive papers, were assessed in R3-stage soybeans in an 18-m wind tunnel using XR (perpendicular spray) and 3D (38° angle) flat fan nozzles under varying air speeds and boom travel directions in the wind tunnel. Potted plants were placed in the wind tunnel to mimic soybeans grown in field conditions. Droplet sizes of the nozzles were measured using a laser imaging particle sizing system. Airflow velocity and turbulence within the soybean canopy were investigated with a 3-D hot-film anemometer system. The results indicated that wind and boom direction were the main influential factors for spray coverage and deposition. The top canopy position, exposed to the highest air-turbulence intensity, received the greatest deposition, whereas the middle and bottom positions, characterized by lower turbulence, exhibited sharp declines in both deposition and coverage regardless of treatment. The 3D nozzle provided greater coverage and deposition than the XR nozzle only under no-wind conditions; however, under wind conditions, equivalent performance was observed from both nozzles. Therefore, it was essential to incorporate wind conditions and canopy structures into consideration when choosing nozzles to maximize spray penetration and achieve efficient and effective spray applications for soybeans. Full article

Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging because of energy constraints, communication reliability, and real-time processing requirements. This paper presents a modular, solar-powered environmental monitoring platform integrating LTE-M communication and TinyML-enabled edge sensing. The proposed system adopts a dual-microcontroller architecture that combines an Arduino Nano 33 BLE for real-time sensor acquisition and edge processing with an Arduino MKR NB 1500 dedicated to low-power wide-area communication. The platform integrates temperature, humidity, atmospheric pressure, rainfall, wind, and light sensors within a scalable framework. Two monitoring stations were deployed in rural regions of Romania to evaluate communication robustness, sensing stability, and energy autonomy. Field results demonstrated reliable LTE-M connectivity (4306 received signal strength indicator [RSSI] samples; mean $-75.51$ dBm) and strong agreement with a regional weather station, with mean deviations of −0.71 °C (temperature), $4.98\%$ (humidity), and a stable pressure offset of $-9.58$ hPa attributable to altitude differences. Despite a total system cost of €315, the platform achieved measurement performance comparable to that of professional meteorological stations while maintaining long-term solar-powered operation. The proposed architecture provides a scalable and cost-effective solution for distributed smart agriculture and environmental monitoring applications. Full article

With the increasing demand for earth observation and communication missions, semi-rigid airships have emerged as critical aerial platforms due to their long endurance and high payload capacity. However, high-precision dynamic modeling and robust autonomous flight control remain challenging because of large hull volume and strong aerodynamic nonlinearities. This study proposes an integrated framework combining computational fluid dynamics (CFD) aerodynamic modeling with full-envelope gain scheduling control. First, nonlinear aerodynamic characteristics over wide ranges of angles of attack and sideslip are identified via CFD simulation, and a six-degree-of-freedom (6-DOF) nonlinear dynamic model incorporating added-mass effects is established. Subsequently, a gain scheduling linear quadratic regulator (LQR) controller is then designed using airspeed, climb rate, and yaw rate as scheduling variables, enabling coordinated control allocation between low-speed thrust vectoring and high-speed aerodynamic surfaces. Simulation results demonstrate improved three-dimensional (3D) path following performance and smooth flight mode transitions. The mean absolute errors (MAEs) in altitude, airspeed, and heading are limited to 0.711 m, 0.028 m/s, and 2.377°, respectively. Furthermore, the system’s robustness is validated under composite wind disturbances, confirming effectiveness of the proposed approach across the full flight envelope. Full article

This paper investigates the electromagnetic performance of an outer-rotor spoke-type permanent magnet synchronous generator intended for small wind turbine applications below 5 kW. The study focuses on the influence of structural ferromagnetic components on magnetic flux distribution and overall machine performance. The generator was initially designed and optimized using 2D finite element analysis, followed by a comprehensive 3D model to account for axial flux leakage and structural details; particular attention was given to the fastening screws used. Experimental validation on a dedicated laboratory test bench confirms the accuracy of the 3D model, mainly at lower wind speeds. The results highlight the necessity of including structural components in three-dimensional electromagnetic modeling for accurate performance prediction of flux-concentrating wind turbine generators. Full article

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks. Full article

A coupled non-destructive testing (NDT) workflow is proposed that integrates active infrared thermography (IRT) with laser-scanning-based reverse engineering (RE) to increase the reliability of detecting and interpreting damage in composite wind turbine blades across laboratory specimens and real components. IRT provides rapid, image-based qualitative localisation of potential anomalies, while 3D scan analysis supplies quantitative, geometry-aware verification and measurement of defect magnitude, reducing both false positives (design-related thermal signatures) and false negatives (weak thermal contrast). On polystyrene-filled profiles, IRT alone produced thermal anomalies unrelated to delamination; co-registered scan maps identified or ruled out local indentation, correctly attributing heat-flow patterns to internal design rather than damage. Outcome: the fused method disambiguates thermal indications and quantifies defect magnitude. On a vertical-axis wind turbine (VAWT) blade, the integration distinguished genuine geometric change from architectural effects under unknown internal structure and without CAD/reference scans, preventing false calls. For three horizontal-axis wind turbine (HAWT) blades, fleet-level scan comparison detected a significant tip deviation despite no clear local IRT anomalies, demonstrating complementary roles: scan = global quantitative homogeneity; and IRT = local qualitative verification. These findings operationalise thermal–geometric cross-validation and outline a path toward UAV-enabled inspections combining passive IRT and laser scanning for hard-to-access structures under real environmental conditions. Full article

Agriculture is rapidly becoming a data-driven field where automation relies on transforming 2D images into accurate 3D models. However, selecting the most effective method remains challenging due to the unconstrained nature of the environment. This review assesses the effectiveness of geometry-based, sensor-based, and learning-based reconstruction methodologies in agricultural settings. We analyze photogrammetric pipelines, active sensing, and neural rendering methods based on their geometric accuracy, data processing speed, and field performance against wind or occlusion. Our analysis indicates that while Light Detection and Ranging (LiDAR) is highly accurate, it is too expensive for widespread adoption. Conversely, geometry-based methods are inexpensive but struggle with complex biological structures. Learning-based methods, especially 3D Gaussian Splatting (3DGS), have revolutionized the field by enabling a balance between visual fidelity and real-time inference speed. We conclude that the best chance for scalability and accuracy lies in hybrid pipelines that integrate Vision Foundation Models (VFMs) with geometric priors. We believe that “hybrid intelligence” systems, such as edge-native 3D Gaussian Splatting combined with semantic priors, are the future of 3D reconstruction. These systems will enable the creation of real-time, spatiotemporal (4D) digital twins that drive automated decision-making in precision agriculture. Full article

Short-term forecasting of solar and wind power generation is critical for smart grid management but challenging due to non-stationarity and extreme generation events. This study addresses a multi-task learning problem: regression-based forecasting of power output and binary detection of extreme events defined by a quantile-based threshold (q = 0.90). A hybrid spatio-temporal model, DP-STH++, is proposed, implementing parallel causal fusion of LSTM, GRU, a causal Conv1D stack, and a lightweight causal transformer. The architecture employs regression and classification heads, while an uncertainty-weighted mechanism stabilizes multitask optimization in the regression tasks; extreme event detection performance is evaluated using AUC. Training and evaluation follow a leakage-safe protocol with chronological data processing, calendar feature integration, time-aware splitting, and training-only estimation of scaling parameters and extreme thresholds. Experimental results obtained with a one-hour forecasting horizon and a 24 h context window demonstrate that DP-STH++ achieves the best regression performance on the hold-out set (RMSE = 257.18, MAE = 174.86–287.90, MASE = 0.2438, R² = 0.9440) and the highest extreme event detection accuracy (AUC = 0.9896), ranking 1st among all compared architectures. In time-series cross-validation, the model retains the leading position with a mean MASE = 0.3883 and AUC = 0.9709. The advantages are particularly pronounced for wind power forecasting, where DP-STH++ simultaneously minimizes regression errors and maximizes AUC = 0.9880–0.9908. Full article

The Mexican Tropical Pacific (MTP) is a key component of the Eastern Tropical North Pacific Oxygen Minimum Zone, yet its carbonate system variability remains poorly constrained. This study examines wind-driven circulation effects on dissolved oxygen (DO) and the carbonate system —dissolved inorganic carbon (DIC), total alkalinity (TA), total-scale pH (pH_T), partial pressure of CO₂ in seawater (pCO_2w) and air–sea CO₂ fluxes (FCO₂)— in the Gulf of Tehuantepec (GT) and Tehuantepec Bowl (TB). Hydrographic data and discrete water samples were collected at 50 oceanographic stations during March 2020. Principal Component Analysis (PCA) identifies wind-driven circulation as the primary control of biogeochemical variability. Tehuano wind events and mesoscale eddies promoted upwelling of low-oxygen (DO < 20 µmol kg⁻¹) and high-DIC (>2200 µmol kg⁻¹) waters to 50 m depth in the central GT, while downwelling conditions prevailed in the TB. Stoichiometric analysis revealed DIC-DO coupling (slope = −1.39). Overall, the MTP acted as CO₂ source (FCO₂ ranging from −1.92 to 24.11 mmol m⁻² d⁻¹), with enhanced emissions linked to eddy-induced upwelling. This study provides the first integrated characterization of the carbonate system across both the GT and TB. Full article

Accurate reproduction of deterministic gusts in wind tunnels is essential for studying unsteady aerodynamics and aeroelastic response in aircraft, uninhabited aerial vehicles, and wind turbines. This work presents the design, experimental characterization, and numerical modelling of a low-speed gust generator based on oscillating vanes, capable of producing high-amplitude gusts in strongly unsteady flow regimes. Cross-flow hot-wire measurements are combined with time-accurate computational fluid dynamics simulations to analyze gust formation and propagation. Classical ‘1-cos’ gusts are shown to exhibit pronounced negative velocity peaks associated with start–stop vortex shedding. A modified vane motion protocol is proposed that significantly reduces the negative peak factor while preserving a substantial gust ratio over a wide range of reduced frequencies. Measurements are supplemented with computational fluid dynamics (CFD) simulations. The CFD study included 2D and 3D URANS as well as higher fidelity DES simulations. Flow-field analysis reveals that secondary variations in gust angle arise from nonlinear interactions between vortices shed by adjacent vanes and are influenced by wind-tunnel confinement. The results provide physical insight into the limitations of oscillating-vane gust generators and guidance for the design of high-fidelity gust-generation systems. Full article